All methods are initialized with 100-dimension Glove embeddings [1]}. The hidden state dimensions is 200 for BeerAdvocate, and 100 for MovieReview. We use Adam [2]} as the default optimizer with a learning rate of 0.001. The policy gradient update uses a learning rate of 1e-4. The exploration rate is 0.2.
The aforementioned hyperparameters and the best models to report are selected according to the development set accuracy.
Every compared model is trained on a single V100 GPU.
